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[pre-commit.ci] auto fixes from pre-commit.com hooks

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pre-commit-ci[bot] 2024-11-15 09:26:56 +00:00
parent 84b29c0eed
commit 9049228ff8
1 changed files with 15 additions and 10 deletions
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25
genetic_algorithm/genetic_algorithm_optimization.py
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@ -122,7 +122,7 @@ class GeneticAlgorithm:
if not population_score: if not population_score:
raise ValueError("Population score is empty, cannot select parents.") raise ValueError("Population score is empty, cannot select parents.")
population_score.sort(key=lambda score_tuple: score_tuple[1], reverse=True) population_score.sort(key=lambda score_tuple: score_tuple[1], reverse=True)
selected_count = min(N_SELECTED, len(population_score)) selected_count = min(N_SELECTED, len(population_score))
return [ind for ind, _ in population_score[:selected_count]] return [ind for ind, _ in population_score[:selected_count]]
@ -245,32 +245,37 @@ class GeneticAlgorithm:
for generation in range(self.generations): for generation in range(self.generations):
# Evaluate population fitness (multithreaded) # Evaluate population fitness (multithreaded)
population_score = self.evaluate_population() population_score = self.evaluate_population()
# Ensure population_score isn't empty # Ensure population_score isn't empty
if not population_score: if not population_score:
raise ValueError("Population score is empty. No individuals evaluated.") raise ValueError("Population score is empty. No individuals evaluated.")
# Check the best individual # Check the best individual
best_individual = max(population_score, key=lambda score_tuple: score_tuple[1])[0] best_individual = max(
population_score, key=lambda score_tuple: score_tuple[1]
)[0]
best_fitness = self.fitness(best_individual) best_fitness = self.fitness(best_individual)
# Select parents for next generation # Select parents for next generation
parents = self.select_parents(population_score) parents = self.select_parents(population_score)
next_generation = [] next_generation = []
# Generate offspring using crossover and mutation # Generate offspring using crossover and mutation
for i in range(0, len(parents), 2): for i in range(0, len(parents), 2):
parent1, parent2 = parents[i], parents[(i + 1) % len(parents)] # Wrap around for odd cases parent1, parent2 = (
parents[i],
parents[(i + 1) % len(parents)],
) # Wrap around for odd cases
child1, child2 = self.crossover(parent1, parent2) child1, child2 = self.crossover(parent1, parent2)
next_generation.append(self.mutate(child1)) next_generation.append(self.mutate(child1))
next_generation.append(self.mutate(child2)) next_generation.append(self.mutate(child2))
# Ensure population size remains the same # Ensure population size remains the same
self.population = next_generation[: self.population_size] self.population = next_generation[: self.population_size]
if verbose and generation % 10 == 0: if verbose and generation % 10 == 0:
print(f"Generation {generation}: Best Fitness = {best_fitness}") print(f"Generation {generation}: Best Fitness = {best_fitness}")
return best_individual return best_individual